{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文主要介绍将pytorch模型准确导出为可用的onnx模型。以方便OpenCV Dnn,NCNN,MNN,TensorRT等框架调用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 使用说明\n",
    "本文示例为调用pytorch预训练的mobilenetv2模型，将其导出为onnx模型。主要步骤如下：\n",
    "1. 读取模型\n",
    "2. 检测图像\n",
    "3. 导出为onnx模型\n",
    "4. 模型测试\n",
    "5. 模型简化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 需要调用的头文件\n",
    "import torch\n",
    "from torchvision import models\n",
    "import cv2\n",
    "import numpy as np\n",
    "from torchsummary import summary\n",
    "import onnxruntime\n",
    "from onnxsim import simplify\n",
    "import onnx\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 判断使用CPU还是GPU\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 读取模型\n",
    "该部分主要为调用训练好的模型。主要内容如下\n",
    "1. 直接读取预训练模型\n",
    "2. 将模型转换为推理模型\n",
    "3. 查看模型的结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 1 读取模型 -----\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 32, 112, 112]             864\n",
      "       BatchNorm2d-2         [-1, 32, 112, 112]              64\n",
      "             ReLU6-3         [-1, 32, 112, 112]               0\n",
      "            Conv2d-4         [-1, 32, 112, 112]             288\n",
      "       BatchNorm2d-5         [-1, 32, 112, 112]              64\n",
      "             ReLU6-6         [-1, 32, 112, 112]               0\n",
      "            Conv2d-7         [-1, 16, 112, 112]             512\n",
      "       BatchNorm2d-8         [-1, 16, 112, 112]              32\n",
      "  InvertedResidual-9         [-1, 16, 112, 112]               0\n",
      "           Conv2d-10         [-1, 96, 112, 112]           1,536\n",
      "      BatchNorm2d-11         [-1, 96, 112, 112]             192\n",
      "            ReLU6-12         [-1, 96, 112, 112]               0\n",
      "           Conv2d-13           [-1, 96, 56, 56]             864\n",
      "      BatchNorm2d-14           [-1, 96, 56, 56]             192\n",
      "            ReLU6-15           [-1, 96, 56, 56]               0\n",
      "           Conv2d-16           [-1, 24, 56, 56]           2,304\n",
      "      BatchNorm2d-17           [-1, 24, 56, 56]              48\n",
      " InvertedResidual-18           [-1, 24, 56, 56]               0\n",
      "           Conv2d-19          [-1, 144, 56, 56]           3,456\n",
      "      BatchNorm2d-20          [-1, 144, 56, 56]             288\n",
      "            ReLU6-21          [-1, 144, 56, 56]               0\n",
      "           Conv2d-22          [-1, 144, 56, 56]           1,296\n",
      "      BatchNorm2d-23          [-1, 144, 56, 56]             288\n",
      "            ReLU6-24          [-1, 144, 56, 56]               0\n",
      "           Conv2d-25           [-1, 24, 56, 56]           3,456\n",
      "      BatchNorm2d-26           [-1, 24, 56, 56]              48\n",
      " InvertedResidual-27           [-1, 24, 56, 56]               0\n",
      "           Conv2d-28          [-1, 144, 56, 56]           3,456\n",
      "      BatchNorm2d-29          [-1, 144, 56, 56]             288\n",
      "            ReLU6-30          [-1, 144, 56, 56]               0\n",
      "           Conv2d-31          [-1, 144, 28, 28]           1,296\n",
      "      BatchNorm2d-32          [-1, 144, 28, 28]             288\n",
      "            ReLU6-33          [-1, 144, 28, 28]               0\n",
      "           Conv2d-34           [-1, 32, 28, 28]           4,608\n",
      "      BatchNorm2d-35           [-1, 32, 28, 28]              64\n",
      " InvertedResidual-36           [-1, 32, 28, 28]               0\n",
      "           Conv2d-37          [-1, 192, 28, 28]           6,144\n",
      "      BatchNorm2d-38          [-1, 192, 28, 28]             384\n",
      "            ReLU6-39          [-1, 192, 28, 28]               0\n",
      "           Conv2d-40          [-1, 192, 28, 28]           1,728\n",
      "      BatchNorm2d-41          [-1, 192, 28, 28]             384\n",
      "            ReLU6-42          [-1, 192, 28, 28]               0\n",
      "           Conv2d-43           [-1, 32, 28, 28]           6,144\n",
      "      BatchNorm2d-44           [-1, 32, 28, 28]              64\n",
      " InvertedResidual-45           [-1, 32, 28, 28]               0\n",
      "           Conv2d-46          [-1, 192, 28, 28]           6,144\n",
      "      BatchNorm2d-47          [-1, 192, 28, 28]             384\n",
      "            ReLU6-48          [-1, 192, 28, 28]               0\n",
      "           Conv2d-49          [-1, 192, 28, 28]           1,728\n",
      "      BatchNorm2d-50          [-1, 192, 28, 28]             384\n",
      "            ReLU6-51          [-1, 192, 28, 28]               0\n",
      "           Conv2d-52           [-1, 32, 28, 28]           6,144\n",
      "      BatchNorm2d-53           [-1, 32, 28, 28]              64\n",
      " InvertedResidual-54           [-1, 32, 28, 28]               0\n",
      "           Conv2d-55          [-1, 192, 28, 28]           6,144\n",
      "      BatchNorm2d-56          [-1, 192, 28, 28]             384\n",
      "            ReLU6-57          [-1, 192, 28, 28]               0\n",
      "           Conv2d-58          [-1, 192, 14, 14]           1,728\n",
      "      BatchNorm2d-59          [-1, 192, 14, 14]             384\n",
      "            ReLU6-60          [-1, 192, 14, 14]               0\n",
      "           Conv2d-61           [-1, 64, 14, 14]          12,288\n",
      "      BatchNorm2d-62           [-1, 64, 14, 14]             128\n",
      " InvertedResidual-63           [-1, 64, 14, 14]               0\n",
      "           Conv2d-64          [-1, 384, 14, 14]          24,576\n",
      "      BatchNorm2d-65          [-1, 384, 14, 14]             768\n",
      "            ReLU6-66          [-1, 384, 14, 14]               0\n",
      "           Conv2d-67          [-1, 384, 14, 14]           3,456\n",
      "      BatchNorm2d-68          [-1, 384, 14, 14]             768\n",
      "            ReLU6-69          [-1, 384, 14, 14]               0\n",
      "           Conv2d-70           [-1, 64, 14, 14]          24,576\n",
      "      BatchNorm2d-71           [-1, 64, 14, 14]             128\n",
      " InvertedResidual-72           [-1, 64, 14, 14]               0\n",
      "           Conv2d-73          [-1, 384, 14, 14]          24,576\n",
      "      BatchNorm2d-74          [-1, 384, 14, 14]             768\n",
      "            ReLU6-75          [-1, 384, 14, 14]               0\n",
      "           Conv2d-76          [-1, 384, 14, 14]           3,456\n",
      "      BatchNorm2d-77          [-1, 384, 14, 14]             768\n",
      "            ReLU6-78          [-1, 384, 14, 14]               0\n",
      "           Conv2d-79           [-1, 64, 14, 14]          24,576\n",
      "      BatchNorm2d-80           [-1, 64, 14, 14]             128\n",
      " InvertedResidual-81           [-1, 64, 14, 14]               0\n",
      "           Conv2d-82          [-1, 384, 14, 14]          24,576\n",
      "      BatchNorm2d-83          [-1, 384, 14, 14]             768\n",
      "            ReLU6-84          [-1, 384, 14, 14]               0\n",
      "           Conv2d-85          [-1, 384, 14, 14]           3,456\n",
      "      BatchNorm2d-86          [-1, 384, 14, 14]             768\n",
      "            ReLU6-87          [-1, 384, 14, 14]               0\n",
      "           Conv2d-88           [-1, 64, 14, 14]          24,576\n",
      "      BatchNorm2d-89           [-1, 64, 14, 14]             128\n",
      " InvertedResidual-90           [-1, 64, 14, 14]               0\n",
      "           Conv2d-91          [-1, 384, 14, 14]          24,576\n",
      "      BatchNorm2d-92          [-1, 384, 14, 14]             768\n",
      "            ReLU6-93          [-1, 384, 14, 14]               0\n",
      "           Conv2d-94          [-1, 384, 14, 14]           3,456\n",
      "      BatchNorm2d-95          [-1, 384, 14, 14]             768\n",
      "            ReLU6-96          [-1, 384, 14, 14]               0\n",
      "           Conv2d-97           [-1, 96, 14, 14]          36,864\n",
      "      BatchNorm2d-98           [-1, 96, 14, 14]             192\n",
      " InvertedResidual-99           [-1, 96, 14, 14]               0\n",
      "          Conv2d-100          [-1, 576, 14, 14]          55,296\n",
      "     BatchNorm2d-101          [-1, 576, 14, 14]           1,152\n",
      "           ReLU6-102          [-1, 576, 14, 14]               0\n",
      "          Conv2d-103          [-1, 576, 14, 14]           5,184\n",
      "     BatchNorm2d-104          [-1, 576, 14, 14]           1,152\n",
      "           ReLU6-105          [-1, 576, 14, 14]               0\n",
      "          Conv2d-106           [-1, 96, 14, 14]          55,296\n",
      "     BatchNorm2d-107           [-1, 96, 14, 14]             192\n",
      "InvertedResidual-108           [-1, 96, 14, 14]               0\n",
      "          Conv2d-109          [-1, 576, 14, 14]          55,296\n",
      "     BatchNorm2d-110          [-1, 576, 14, 14]           1,152\n",
      "           ReLU6-111          [-1, 576, 14, 14]               0\n",
      "          Conv2d-112          [-1, 576, 14, 14]           5,184\n",
      "     BatchNorm2d-113          [-1, 576, 14, 14]           1,152\n",
      "           ReLU6-114          [-1, 576, 14, 14]               0\n",
      "          Conv2d-115           [-1, 96, 14, 14]          55,296\n",
      "     BatchNorm2d-116           [-1, 96, 14, 14]             192\n",
      "InvertedResidual-117           [-1, 96, 14, 14]               0\n",
      "          Conv2d-118          [-1, 576, 14, 14]          55,296\n",
      "     BatchNorm2d-119          [-1, 576, 14, 14]           1,152\n",
      "           ReLU6-120          [-1, 576, 14, 14]               0\n",
      "          Conv2d-121            [-1, 576, 7, 7]           5,184\n",
      "     BatchNorm2d-122            [-1, 576, 7, 7]           1,152\n",
      "           ReLU6-123            [-1, 576, 7, 7]               0\n",
      "          Conv2d-124            [-1, 160, 7, 7]          92,160\n",
      "     BatchNorm2d-125            [-1, 160, 7, 7]             320\n",
      "InvertedResidual-126            [-1, 160, 7, 7]               0\n",
      "          Conv2d-127            [-1, 960, 7, 7]         153,600\n",
      "     BatchNorm2d-128            [-1, 960, 7, 7]           1,920\n",
      "           ReLU6-129            [-1, 960, 7, 7]               0\n",
      "          Conv2d-130            [-1, 960, 7, 7]           8,640\n",
      "     BatchNorm2d-131            [-1, 960, 7, 7]           1,920\n",
      "           ReLU6-132            [-1, 960, 7, 7]               0\n",
      "          Conv2d-133            [-1, 160, 7, 7]         153,600\n",
      "     BatchNorm2d-134            [-1, 160, 7, 7]             320\n",
      "InvertedResidual-135            [-1, 160, 7, 7]               0\n",
      "          Conv2d-136            [-1, 960, 7, 7]         153,600\n",
      "     BatchNorm2d-137            [-1, 960, 7, 7]           1,920\n",
      "           ReLU6-138            [-1, 960, 7, 7]               0\n",
      "          Conv2d-139            [-1, 960, 7, 7]           8,640\n",
      "     BatchNorm2d-140            [-1, 960, 7, 7]           1,920\n",
      "           ReLU6-141            [-1, 960, 7, 7]               0\n",
      "          Conv2d-142            [-1, 160, 7, 7]         153,600\n",
      "     BatchNorm2d-143            [-1, 160, 7, 7]             320\n",
      "InvertedResidual-144            [-1, 160, 7, 7]               0\n",
      "          Conv2d-145            [-1, 960, 7, 7]         153,600\n",
      "     BatchNorm2d-146            [-1, 960, 7, 7]           1,920\n",
      "           ReLU6-147            [-1, 960, 7, 7]               0\n",
      "          Conv2d-148            [-1, 960, 7, 7]           8,640\n",
      "     BatchNorm2d-149            [-1, 960, 7, 7]           1,920\n",
      "           ReLU6-150            [-1, 960, 7, 7]               0\n",
      "          Conv2d-151            [-1, 320, 7, 7]         307,200\n",
      "     BatchNorm2d-152            [-1, 320, 7, 7]             640\n",
      "InvertedResidual-153            [-1, 320, 7, 7]               0\n",
      "          Conv2d-154           [-1, 1280, 7, 7]         409,600\n",
      "     BatchNorm2d-155           [-1, 1280, 7, 7]           2,560\n",
      "           ReLU6-156           [-1, 1280, 7, 7]               0\n",
      "         Dropout-157                 [-1, 1280]               0\n",
      "          Linear-158                 [-1, 1000]       1,281,000\n",
      "================================================================\n",
      "Total params: 3,504,872\n",
      "Trainable params: 3,504,872\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.57\n",
      "Forward/backward pass size (MB): 152.87\n",
      "Params size (MB): 13.37\n",
      "Estimated Total Size (MB): 166.81\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# ----- 1 读取模型\n",
    "print(\"----- 1 读取模型 -----\")\n",
    "# 载入模型并读取权重\n",
    "model = models.mobilenet_v2(pretrained=True)\n",
    "# 将模型转换为推理模式\n",
    "model.eval()\n",
    "# 查看模型的结构，(3,224,224)为模型的图像输入\n",
    "summary(model, (3, 224, 224))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 检测图像\n",
    "该部分主要为检测图像，查看模型结果。一般来说pytorch导出的onnx模型都是用于C++调用，所以基于OpenCV直接读取图像，进行图像通道转换以及图像归一化以模拟实际C++调用情况，而不是用pillow和pytorch的transform。一般来说C++提供的图像都是经由OpenCV调用而来。主要内容如下：\n",
    "1. 基于OpenCV读取图像,进行通道转换\n",
    "2. 将图像进行归一化\n",
    "3. 进行模型推理，查看结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 2 检测图像 -----\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['预测标签为: 331,预测概率为:54.409969329833984;', '预测标签为: 330,预测概率为:33.62083435058594;', '预测标签为: 332,预测概率为:11.84182071685791;', '预测标签为: 263,预测概率为:0.05221949517726898;', '预测标签为: 264,预测概率为:0.027525480836629868;']\n"
     ]
    }
   ],
   "source": [
    "# ----- 2 检测图像\n",
    "print(\"----- 2 检测图像 -----\")\n",
    "# 待检测图像路径 \n",
    "img_path = './image/rabbit.jpg'\n",
    "\n",
    "# 读取图像\n",
    "img = cv2.imread(img_path)\n",
    "# 图像通道转换\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "# 展示图像\n",
    "plt.imshow(img)\n",
    "plt.show()\n",
    "# 图像大小重置为模型输入图像大小\n",
    "img = cv2.resize(img, (224, 224))\n",
    "\n",
    "# 图像归一化\n",
    "mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)\n",
    "std = np.array([0.229, 0.224, 0.225], dtype=np.float32)\n",
    "img = np.array((img / 255.0 - mean) / std, dtype=np.float32)\n",
    "\n",
    "# 图像通道转换\n",
    "img = img.transpose([2, 0, 1])\n",
    "# 获得pytorch需要的输入图像格式NCHW\n",
    "img_ = torch.from_numpy(img).unsqueeze(0)\n",
    "img_ = img_.to(device)\n",
    "# 推理\n",
    "outputs = model(img_)\n",
    "\n",
    "# 得到预测结果，并且按概率从大到小排序\n",
    "_, indices = torch.sort(outputs, descending=True)\n",
    "# 返回top5每个预测标签的百分数\n",
    "percentage = torch.nn.functional.softmax(outputs, dim=1)[0] * 100\n",
    "print([\"预测标签为: {},预测概率为:{};\".format(idx, percentage[idx].item()) for idx in indices[0][:5]])\n",
    "\n",
    "# 保存/载入整个pytorch模型\n",
    "# torch.save(model, 'model.ckpt')\n",
    "# model = torch.load('model.ckpt')\n",
    "\n",
    "# 仅仅保存/载入pytorch模型的参数\n",
    "# torch.save(model.state_dict(), 'params.ckpt')\n",
    "# model.load_state_dict(torch.load('params.ckpt'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 导出为onnx模型\n",
    "该部分主要为导出onnx模型，两行代码就可以搞定,onnx模型导出路径为当前目录下mobilenet_v2.onnx。具体如下：\n",
    "```python\n",
    "x = torch.rand(1, 3, 224, 224)\n",
    "torch_out = torch.onnx._export(model, x, output_name, export_params=True,\n",
    "                               input_names=[\"input\"], output_names=[\"output\"])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 3 导出为onnx模型 -----\n",
      "模型导出成功\n"
     ]
    }
   ],
   "source": [
    "# ---- 3 导出为onnx模型\n",
    "print(\"----- 3 导出为onnx模型 -----\")\n",
    "# An example input you would normally provide to your model's forward() method\n",
    "# x为输入图像，格式为pytorch的NCHW格式；1为图像数一般不需要修改；3为通道数；224，224为图像高宽；\n",
    "x = torch.rand(1, 3, 224, 224)\n",
    "# 模型输出名\n",
    "output_name = \"mobilenet_v2.onnx\"\n",
    "# Export the model\n",
    "# 导出为onnx模型\n",
    "# model为模型，x为模型输入，\"mobilenet_v2.onnx\"为onnx输出名，export_params表示是否保存模型参数\n",
    "# input_names为onnx模型输入节点名字，需要输入列表\n",
    "# output_names为onnx模型输出节点名字，需要输入列表；如果是多输出修改为output_names=[\"output1\",\"output2\"]\n",
    "torch_out = torch.onnx._export(model, x, output_name, export_params=True,\n",
    "                               input_names=[\"input\"], output_names=[\"output\"])\n",
    "print(\"模型导出成功\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 模型测试\n",
    "该部分主要为测试模型，一般可以跳过，不需要这部分代码，通常模型转换不会出错。另外onnx模型可以通过[Netron](https://github.com/lutzroeder/Netron)查看结构。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 4 模型测试 -----\n",
      "模型测试成功\n"
     ]
    }
   ],
   "source": [
    "# ---- 4 模型测试(可跳过)\n",
    "print(\"----- 4 模型测试 -----\")\n",
    "\n",
    "\n",
    "# 可以跳过该步骤，一般不会有问题\n",
    "\n",
    "# 检查输出\n",
    "def check_onnx_output(filename, input_data, torch_output):\n",
    "    session = onnxruntime.InferenceSession(filename)\n",
    "    input_name = session.get_inputs()[0].name\n",
    "    result = session.run([], {input_name: input_data.numpy()})\n",
    "    for test_result, gold_result in zip(result, torch_output.values()):\n",
    "        np.testing.assert_almost_equal(\n",
    "            gold_result.cpu().numpy(), test_result, decimal=3,\n",
    "        )\n",
    "    return result\n",
    "\n",
    "\n",
    "# 检查模型\n",
    "def check_onnx_model(model, onnx_filename, input_image):\n",
    "    with torch.no_grad():\n",
    "        torch_out = {\"output\": model(input_image)}\n",
    "    check_onnx_output(onnx_filename, input_image, torch_out)\n",
    "    onnx_model = onnx.load(onnx_filename)\n",
    "    onnx.checker.check_model(onnx_model)\n",
    "    print(\"模型测试成功\")\n",
    "    return onnx_model\n",
    "\n",
    "# 检测导出的onnx模型是否完整\n",
    "# 一般出现问题程序直接报错，不过很少出现问题\n",
    "onnx_model = check_onnx_model(model, output_name, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 模型简化\n",
    "一般来说导出后的onnx模型会有一堆冗余操作，需要简化。推荐使用[onnx-simplifier](https://github.com/daquexian/onnx-simplifier)进行onnx模型简化。onnx简化模型导出路径为当前目录下mobilenet_v2.onnxsim.onnx\n",
    "调用onnx-simplifier有三种办法：\n",
    "1. 调用代码，调用onnx-simplifier的simplify接口\n",
    "2. 命令行简化，直接输入python3 -m onnxsim input_onnx_model output_onnx_model\n",
    "3. 在线调用，调用[onnx-simplifier](https://github.com/daquexian/onnx-simplifier)作者的[https://convertmodel.com/](https://convertmodel.com/)直接进行模型简化。\n",
    "\n",
    "具体来说推荐第三种在线使用，第三种在线调用方便，还能将onnx模型转换为ncnn,mnn等模型格式。\n",
    "\n",
    "P.S. onnx-simplifier对于高版本pytorch不那么支持，转换可能失败，所以设置skip_fuse_bn=True跳过融合bn层。这种情况下onnx-simplifier转换出来的onnx模型可能比转换前的模型大，原因是补充了shape信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 5 模型简化 -----\n",
      "模型简化成功\n"
     ]
    }
   ],
   "source": [
    "# ----- 5 模型简化\n",
    "print(\"----- 5 模型简化 -----\")\n",
    "# 基于onnx-simplifier简化模型，https://github.com/daquexian/onnx-simplifier\n",
    "# 也可以命令行输入python3 -m onnxsim input_onnx_model output_onnx_model\n",
    "# 或者使用在线网站直接转换https://convertmodel.com/\n",
    "\n",
    "# 输出模型名\n",
    "filename = output_name + \"sim.onnx\"\n",
    "# 简化模型\n",
    "# 设置skip_fuse_bn=True表示跳过融合bn层，pytorch高版本融合bn层会出错\n",
    "simplified_model, check = simplify(onnx_model, skip_fuse_bn=True)\n",
    "onnx.save_model(simplified_model, filename)\n",
    "onnx.checker.check_model(simplified_model)\n",
    "# 如果出错\n",
    "assert check, \"简化模型失败\"\n",
    "print(\"模型简化成功\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 全部代码\n",
    "全部工程代码如下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----- 1 读取模型 -----\n",
      "----- 2 检测图像 -----\n",
      "['预测标签为: 331,预测概率为:54.409969329833984;', '预测标签为: 330,预测概率为:33.62083435058594;', '预测标签为: 332,预测概率为:11.84182071685791;', '预测标签为: 263,预测概率为:0.05221949517726898;', '预测标签为: 264,预测概率为:0.027525480836629868;']\n",
      "----- 3 导出为onnx模型 -----\n",
      "模型导出成功\n",
      "----- 4 模型测试 -----\n",
      "模型测试成功\n",
      "----- 5 模型简化 -----\n",
      "模型简化成功\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "Created on Tue Dec  8 19:44:42 2020\n",
    "\n",
    "@author: luohenyueji\n",
    "\"\"\"\n",
    "\n",
    "import torch\n",
    "from torchvision import models\n",
    "import cv2\n",
    "import numpy as np\n",
    "from torchsummary import summary\n",
    "import onnxruntime\n",
    "from onnxsim import simplify\n",
    "import onnx\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 判断使用CPU还是GPU\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# ----- 1 读取模型\n",
    "print(\"----- 1 读取模型 -----\")\n",
    "# 载入模型并读取权重\n",
    "model = models.mobilenet_v2(pretrained=True)\n",
    "# 将模型转换为推理模式\n",
    "model.eval()\n",
    "# 查看模型的结构，(3,224,224)为模型的图像输入\n",
    "# summary(model, (3, 224, 224))\n",
    "\n",
    "# ----- 2 检测图像\n",
    "print(\"----- 2 检测图像 -----\")\n",
    "# 待检测图像路径 \n",
    "img_path = './image/rabbit.jpg'\n",
    "\n",
    "# 读取图像\n",
    "img = cv2.imread(img_path)\n",
    "# 图像通道转换\n",
    "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "# 展示图像\n",
    "# plt.imshow(img)\n",
    "# plt.show()\n",
    "# 图像大小重置为模型输入图像大小\n",
    "img = cv2.resize(img, (224, 224))\n",
    "\n",
    "# 图像归一化\n",
    "mean = np.array([0.485, 0.456, 0.406], dtype=np.float32)\n",
    "std = np.array([0.229, 0.224, 0.225], dtype=np.float32)\n",
    "img = np.array((img / 255.0 - mean) / std, dtype=np.float32)\n",
    "\n",
    "# 图像通道转换\n",
    "img = img.transpose([2, 0, 1])\n",
    "# 获得pytorch需要的输入图像格式NCHW\n",
    "img_ = torch.from_numpy(img).unsqueeze(0)\n",
    "img_ = img_.to(device)\n",
    "# 推理\n",
    "outputs = model(img_)\n",
    "\n",
    "# 得到预测结果，并且按概率从大到小排序\n",
    "_, indices = torch.sort(outputs, descending=True)\n",
    "# 返回top5每个预测标签的百分数\n",
    "percentage = torch.nn.functional.softmax(outputs, dim=1)[0] * 100\n",
    "print([\"预测标签为: {},预测概率为:{};\".format(idx, percentage[idx].item()) for idx in indices[0][:5]])\n",
    "\n",
    "# 保存/载入整个pytorch模型\n",
    "# torch.save(model, 'model.ckpt')\n",
    "# model = torch.load('model.ckpt')\n",
    "\n",
    "# 仅仅保存/载入pytorch模型的参数\n",
    "# torch.save(model.state_dict(), 'params.ckpt')\n",
    "# model.load_state_dict(torch.load('params.ckpt'))\n",
    "\n",
    "# ---- 3 导出为onnx模型\n",
    "print(\"----- 3 导出为onnx模型 -----\")\n",
    "# An example input you would normally provide to your model's forward() method\n",
    "# x为输入图像，格式为pytorch的NCHW格式；1为图像数一般不需要修改；3为通道数；224，224为图像高宽；\n",
    "x = torch.rand(1, 3, 224, 224)\n",
    "# 模型输出名\n",
    "output_name = \"mobilenet_v2.onnx\"\n",
    "# Export the model\n",
    "# 导出为onnx模型\n",
    "# model为模型，x为模型输入，\"mobilenet_v2.onnx\"为onnx输出名，export_params表示是否保存模型参数\n",
    "# input_names为onnx模型输入节点名字，需要输入列表\n",
    "# output_names为onnx模型输出节点名字，需要输入列表；如果是多输出修改为output_names=[\"output1\",\"output2\"]\n",
    "torch_out = torch.onnx._export(model, x, output_name, export_params=True,\n",
    "                               input_names=[\"input\"], output_names=[\"output\"])\n",
    "print(\"模型导出成功\")\n",
    "\n",
    "# ---- 4 模型测试(可跳过)\n",
    "print(\"----- 4 模型测试 -----\")\n",
    "\n",
    "\n",
    "# 可以跳过该步骤，一般不会有问题\n",
    "\n",
    "# 检查输出\n",
    "def check_onnx_output(filename, input_data, torch_output):\n",
    "    session = onnxruntime.InferenceSession(filename)\n",
    "    input_name = session.get_inputs()[0].name\n",
    "    result = session.run([], {input_name: input_data.numpy()})\n",
    "    for test_result, gold_result in zip(result, torch_output.values()):\n",
    "        np.testing.assert_almost_equal(\n",
    "            gold_result.cpu().numpy(), test_result, decimal=3,\n",
    "        )\n",
    "    return result\n",
    "\n",
    "\n",
    "# 检查模型\n",
    "def check_onnx_model(model, onnx_filename, input_image):\n",
    "    with torch.no_grad():\n",
    "        torch_out = {\"output\": model(input_image)}\n",
    "    check_onnx_output(onnx_filename, input_image, torch_out)\n",
    "    onnx_model = onnx.load(onnx_filename)\n",
    "    onnx.checker.check_model(onnx_model)\n",
    "    print(\"模型测试成功\")\n",
    "    return onnx_model\n",
    "\n",
    "\n",
    "# 检测导出的onnx模型是否完整\n",
    "# 一般出现问题程序直接报错，不过很少出现问题\n",
    "onnx_model = check_onnx_model(model, output_name, x)\n",
    "\n",
    "# ----- 5 模型简化\n",
    "print(\"----- 5 模型简化 -----\")\n",
    "# 基于onnx-simplifier简化模型，https://github.com/daquexian/onnx-simplifier\n",
    "# 也可以命令行输入python3 -m onnxsim input_onnx_model output_onnx_model\n",
    "# 或者使用在线网站直接转换https://convertmodel.com/\n",
    "\n",
    "# 输出模型名\n",
    "filename = output_name + \"sim.onnx\"\n",
    "# 简化模型\n",
    "# 设置skip_fuse_bn=True表示跳过融合bn层，pytorch高版本融合bn层会出错\n",
    "simplified_model, check = simplify(onnx_model, skip_fuse_bn=True)\n",
    "onnx.save_model(simplified_model, filename)\n",
    "onnx.checker.check_model(simplified_model)\n",
    "# 如果出错\n",
    "assert check, \"简化模型失败\"\n",
    "print(\"模型简化成功\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 参考\n",
    "+ [Netron](https://github.com/lutzroeder/Netron)\n",
    "+ [use ncnn with pytorch or onnx](https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx)\n",
    "+ [PyTorch to CoreML model conversion](https://www.learnopencv.com/pytorch-to-coreml-model-conversion/)\n",
    "+ [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)\n",
    "+ [https://convertmodel.com/](https://convertmodel.com/)"
   ]
  }
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